Lung ultrasound (LUS) is a valuable, non-invasive tool for detecting pulmonary congestion in patients with acute heart failure (AHF), with a higher sensitivity relative to physical examination. However, the association between LUS-detected pulmonary congestion and cardiovascular outcomes in patients with ST-segment elevation (STEMI) is not well established. This systematic review and meta-analysis evaluated cardiovascular outcomes in patients with STEMI and congested (wet) or non-congested (dry) lungs by LUS. We searched PubMed, Embase, and Cochrane databases, and conference abstracts for clinical trials evaluating LUS-congestion (LUS+) versus non-LUS-congestion (LUS-) in patients with STEMI. Risk ratios (RRs) and hazard ratios (HR) with 95%CIs were pooled using R software under random-effects models. We also calculated LUS sensitivity, specificity, and area under the curves (AUCs) for the prediction of in-hospital mortality and cardiogenic shock. We included five studies with 1,454 patients. The mean age was 60 to 65 years; 1,066 (73.3%) were male, and 451 (31%) had congested lungs (LUS+). Patients with congestion on LUS had a significantly higher risk of the composite endpoint of death, heart failure, acute coronary syndrome, and cardiogenic shock (HR 4.00; 95%CI 2.12-7.54; p<0.01; Figure 1A). There was also a higher risk of in-hospital mortality (RR 5.09; 95%CI 2.25-11.49; p<0.001; Figure 1B) and cardiogenic shock (RR 5.01; 95%CI 2.47-10.17; p<0.001; Figure 1C) compared to patients with non-congested lungs. Reinfarction was similar between groups (p=0.08; Figure 1D). LUS had high diagnostic accuracy for in-hospital mortality (SROC-AUC: 0.82) and cardiogenic shock (SROC-AUC: 0.77); a high sensitivity (0.84; 95%CI 0.49-0.97; Figure 2A), and moderate specificity (0.78; 95%CI 0.67-0.87; Figure 2A) for in-hospital mortality; and moderate sensitivity (0.75; 95%CI 0.42-0.93; Figure 2B) and specificity (0.76; 95% CI 0.61-0.87; Figure 2B) for cardiogenic shock. Congested lungs on LUS are significantly associated with a higher risk of in-hospital mortality and cardiogenic shock in patients with STEMI. Moreover, LUS has a high AUC for identifying in-hospital mortality and cardiogenic shock in this patient population. Figure 1 Figure 2

We introduce the concept of Diophantine spherical vague set approach to multiple-attribute decision-making. The Spherical vague set is a novel expansion of the vague set and interval valued spherical fuzzy set. Three new concepts have been introduce such as Diophantine spherical vague weighted averaging operator, Diophantine spherical vague weighted geometric operator, generalized Diophantine spherical vague weighted averaging operator and generalized Diophantine spherical vague weighted geometric operator. We provide a numerical example to show how Euclidean distance and Hamming distance interact. Applications of the Diophantine spherical vague number include idempotency, boundedness, commutativity and monotonicity in algebraic operations. They can determine the optimal option and are more well-known and reasonable. Our goal was to identify the optimal choice by comparing expert opinions with the criteria. As a result, the model’s output was more accurate as well as in the range of the natural number . The weighted averaging distance and weighted geometric distance operators are distance measure that is based on aggregating model. By comparing the models under discussion with those suggested in the literature, we hoped to show their worth and reliability. It is possible to find a better solution more quickly, simply, and practically. Our objective was to compare the expert evaluations with the criteria and determine which option was the most suitable. Because they yield more precise solutions, these models are more accurate and more related to models with . To show the superiority and the validity of the proposed aggregation operations, we compared it with the existing method and concluded from the comparison and sensitivity analysis that our proposed technique is more effective and reliable. This investigation yielded some intriguing results.

Network emulators play an important role in testing network systems, applications, and protocols. Emulators bridge the gap between simulation setups that lack realism in results and real-world trials that are accurate but often expensive, non-reproducible, and uncontrollable. This article presents an extended model of the Quantum Key Distribution Network Simulation Module (QKDNetSim) with a model catalog of QKD components and functionalities. We explore emulations of point-to-point connections in QKD networks and the interaction of essential components within QKD nodes. The presented tool will undoubtedly spur future development and teaching, and it is critical for testing novel applications and protocols applied to QKD networks.

BACKGROUND Non-ST segment elevation myocardial infarction (NSTEMI) poses significant challenges in clinical management due to its diverse outcomes. Understanding the prognostic role of hematological parameters and derived ratios in NSTEMI patients could aid in risk stratification and improve patient care. AIM To evaluate the predictive value of hemogram-derived ratios for major adverse cardiovascular events (MACE) in NSTEMI patients, potentially improving clinical outcomes. METHODS A prospective, observational cohort study was conducted in 2021 at the Internal Medicine Clinic of the University Hospital in Tuzla, Bosnia and Herzegovina. The study included 170 patients with NSTEMI, who were divided into a group with MACE and a control group without MACE. Furthermore, the MACE group was subdivided into lethal and non-lethal groups for prognostic analysis. Alongside hematological parameters, an additional 13 hematological-derived ratios (HDRs) were monitored, and their prognostic role was investigated. RESULTS Hematological parameters did not significantly differ between non-ST segment elevation myocardial infarction (NSTEMI) patients with MACE and a control group at T1 and T2. However, significant disparities emerged in HDRs among NSTEMI patients with lethal and non-lethal outcomes post-MACE. Notably, neutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte ratio (PLR) were elevated in lethal outcomes. Furthermore, C-reactive protein-to-lymphocyte ratio (CRP/Ly) at T1 (> 4.737) demonstrated predictive value [odds ratio (OR): 3.690, P = 0.024]. Both NLR at T1 (> 4.076) and T2 (> 4.667) emerged as significant predictors, with NLR at T2 exhibiting the highest diagnostic performance, as indicated by an area under the curve of 0.811 (95%CI: 0.727-0.859) and OR of 4.915 (95%CI: 1.917-12.602, P = 0.001), emphasizing its important role as a prognostic marker. CONCLUSION This study highlights the significant prognostic value of hemogram-derived indexes in predicting MACE among NSTEMI patients. During follow-up, NLR, PLR, and CRP/Ly offer important insights into the inflammatory processes underlying cardiovascular events.

(BHS) Ovaj rad se fokusira na upotrebu mašinskog učenja i korištenje namjenskih baza podataka vještačke inteligencije u svrhu kreiranja rješenja zasnovanih na unaprijeđenom algoritmu za preventivno upravljanje rizicima i predikciju rizika u realnom vremenu. U radu se analiziraju postojeći standardi, njihovi nedostaci i moguća rješenja za unapređenje, kao i struktura i algoritamska osnova ovih sistema, te njihova integracija u postojeće sigurnosne arhitekture i platforme. Obuhvaćena je detekcija prijetnji na osnovu anomalija i analiza ustaljenog korisničkog ponašanja prema zadanim obrascima, procjena rizika i proaktivna detekcija napada. Pravovremena identifikacija i upravljanje rizicima postaju ključni faktori održivosti kompanija i sigurnosti poslovnih i informacionih sistema. Prediktivna analitika, zasnovana na vještačkoj inteligenciji, mašinskom učenju i analizi velikih skupova podataka, donosi transformacijske mogućnosti u oblastima poput industrije, finansija i zdravstva, koje su u savremenoj eri povezane sajber sigurnošću i predikcijom rizika, a koje pomažu donosiocima odluka da efikasnije upravljaju sistemima i zaštite ih. Integrativni pristup usklađivanju ovih tehnologija, posebno u kontekstu organizacione strukture i pravnog okvira, obuhvata pitanja pouzdanosti i transparentnosti modela, odgovornosti za automatizovane odluke, zaštite privatnosti i usklađenosti sa zakonodavstvom. Cilj rada je pružiti sveobuhvatan pregled tehnoloških i metodoloških inovacija u prediktivnoj zaštiti od sajber rizika, te identifikovati pravce budućeg razvoja sa posebnim fokusom na sigurnost, etiku i pouzdanost AI sistema. (ENG) This paper focuses on the use of machine learning and the use of dedicated AI databases to create solutions based on an improved algorithm for preventive risk management, and real-time risk prediction. The paper analyses the existing standard, its shortcomings and solutions for improvement, and the structure and algorithmic basis of these systems, as well as their integration into existing security architectures and platforms. The work includes the detection of threats based on anomalies and the analysis of established user behavior according to given patterns, risk assessment and proactive detection of attacks. Timely identification and management of risks are becoming key factors in corporate sustainability and security of business and information systems. Predictive analytics, based on artificial intelligence, machine learning and big data analytics, bring transformational opportunities in areas such as industry, finance, healthcare, which in the modern era are connected by cybersecurity and risk prediction that help decision makers to manage systems more efficiently and protect them. An integrative approach to harmonizing these technologies, especially considering the organizational structure and legal framework, includes issues of reliability and transparency of models, as well as accountability for automated decisions, privacy protection and compliance with legislation. The aim of the paper is to provide a comprehensive overview of technological and methodological innovations in predictive protection against cyber risks, and to identify directions for future development with a special focus on the security, ethics and reliability of AI systems.

Background: Legal certainty is a guiding principle in all European countries. One of the main tools for achieving legal certainty in Europe is the codification of law. In 2023, Saudi Arabia adopted its first codification of contractual and non-contractual obligations through the Civil Transactions Law (CTL), aiming to achieve greater legal certainty. This shift represents a major shift from a predominantly Shariah-based jurisdiction towards civil law. This research examines whether the enactment of the CTL has influenced the Saudi Commercial Court's interpretation of compensation claims. Methods: A mixed-methods approach was adopted to track citation trends over time and to examine case law documents to confirm the quantitative results. Qualitative empirical analysis, specifically document analysis, was utilised to identify and extract Shariah jurists’ opinions, providing depth to the statistical results. Quantitative empirical methods, including interrupted time series (ITS), were applied to assess whether the compensation provisions in the CTL led to significant shifts in compensation claims decisions. Overall, 2,913 cases decided before the enactment of the CTL and 61 decided under the CTL were analysed in this study. Results and conclusions: The pre-law analysis indicates that courts cited Shariah jurists or general legal principles to establish the liability for compensation. In contrast, post-law analysis suggests a discernible shift, with courts increasingly citing civil law provisions directly, notably Articles 120 and 720 of the CTL. This shift is supported by an increase in overall article citations within compensation judgments, rising from 36% to 62%, supported by the examination of cases decided based on these articles. These findings indicate that the enactment of the Civil Transactions Law has contributed to enhancing the legal certainty in Saudi commercial courts.